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Course Outline

Introduction to Generative AI

  • An overview of generative models and their significance in the financial industry.
  • Different types of generative models: LLMs, GANs, and VAEs.
  • Advantages and limitations within financial contexts.

Generative Adversarial Networks (GANs) for Finance

  • The mechanics of GANs: the interplay between generators and discriminators.
  • Applications in generating synthetic data and simulating fraud scenarios.
  • Case study: Producing realistic transaction data for testing purposes.

Large Language Models (LLMs) and Prompt Engineering

  • How LLMs process and generate financial text.
  • Strategies for designing prompts aimed at forecasting and risk analysis.
  • Practical use cases: summarizing financial reports, Know Your Customer (KYC) processes, and detecting red flags.

Financial Forecasting with Generative AI

  • Time series forecasting utilizing hybrid models that combine LLMs and Machine Learning.
  • Scenario generation and stress testing methodologies.
  • Use case: Predicting revenue by leveraging both structured and unstructured data.

Fraud Detection and Anomaly Identification

  • Employing GANs for detecting anomalies in financial transactions.
  • Identifying emerging fraud patterns through prompt-based LLM workflows.
  • Model evaluation: Distinguishing between false positives and genuine risk indicators.

Regulatory and Ethical Implications

  • Ensuring explainability and transparency in generative AI outputs.
  • Addressing risks related to model hallucination and bias in financial applications.
  • Adhering to regulatory expectations, such as GDPR and Basel guidelines.

Designing Generative AI Use Cases for Financial Institutions

  • Developing business cases to drive internal adoption.
  • Balancing innovation with risk management and compliance obligations.
  • Establishing governance frameworks for the responsible deployment of AI.

Summary and Next Steps

Requirements

  • A foundational understanding of finance and risk management principles.
  • Prior experience with spreadsheets or fundamental data analysis.
  • Familiarity with Python is beneficial, though not mandatory.

Target Audience

  • Risk managers
  • Compliance analysts
  • Financial auditors
 14 Hours

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